Graduate Thesis Or Dissertation
 

Mining Temporal Patterns for Prediction: A Mixed-Integer Programming Approach

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/8049gf09h

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  • In the past two decades, the advancement in data collection and storage have led to the accumulation of complex datasets. Consequently, various industries have sought data-driven solutions to predict and detect anomalies. Temporal patterns have emerged as potential features in prediction models that could improve the performance of the identification of anomalies. Existing state-of-the-art approaches in pattern mining for prediction follow a top-down approach, where pattern-defining parameters (e.g. support thresholds and gap parameters) are first chosen arbitrarily or through expert opinion. Subsequently, patterns are identified based on these predefined parameters, and then a prediction model is constructed using these identified patterns as features. In contrast, our research focuses on a novel and wholistic approach to the discovery of patterns. We propose a methodology that enables the simultaneous and optimal discovery of both pattern-defining parameters and prediction model coefficients. This approach allows these parameters to be determined based on the characteristics of the data and the outcome of interest. To achieve this, we develop a mixed-integer programming (MIP) framework, which optimizes the pattern discovery process effectively called RTP-MIP. We compare our model's results with the existing recent temporal pattern mining (RTP) algorithm using random (RTP-Random) and grid search (RTP-Grid) techniques to select parameters value. Experimental results show that MIP can accurately predict the outcome and optimal values of parameters.
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  • Pending Publication
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  • 2023-09-05 to 2024-04-06

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